Heuristic space diversity control for improved meta-hyper-heuristic performance

This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm in search of greater performance benefits. Evaluation of various strategies on a diverse set of floating-point benchmark problems shows that heuristic space diversity has a significant impact on hyper-heuristic performance. An exponentially increasing strategy (EIHH) obtained the best results. The value of a priori information about constituent algorithm performance on the benchmark set in question was also evaluated. Finally, EIHH demonstrated good performance when compared to a popular population based algorithm portfolio algorithm and the best performing constituent algorithm.

[1]  Sambhavi Tiwari,et al.  Closeness based Hierarchical Particle Swarm Optimizer with Time Varying Acceleration Coefficients , 2015 .

[2]  Jim Smith,et al.  A Memetic Algorithm With Self-Adaptive Local Search: TSP as a case study , 2000, GECCO.

[3]  Mark Sumner,et al.  A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[5]  Edmund K. Burke,et al.  Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems , 2009, J. Oper. Res. Soc..

[6]  He Jiang,et al.  Hyper-Heuristics with Low Level Parameter Adaptation , 2012, Evolutionary Computation.

[7]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[8]  Ben Paechter,et al.  Learning to Solve Bin Packing Problems with an Immune Inspired Hyper-heuristic , 2013, ECAL.

[9]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[10]  Mahdieh Soleymani Baghshah,et al.  Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease , 2015, ArXiv.

[11]  Sancho Salcedo-Sanz,et al.  An evolutionary-based hyper-heuristic approach for the Jawbreaker puzzle , 2013, Applied Intelligence.

[12]  Frédéric Saubion,et al.  Using Local Search with adaptive operator selection to solve the Progressive Party Problem , 2013, 2013 IEEE Congress on Evolutionary Computation.

[13]  Kay Chen Tan,et al.  A Multi-Facet Survey on Memetic Computation , 2011, IEEE Transactions on Evolutionary Computation.

[14]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[15]  Andries Petrus Engelbrecht,et al.  Investigating the impact of alternative evolutionary selection strategies on multi-method global optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[16]  Andries Petrus Engelbrecht,et al.  Multi-method algorithms: Investigating the entity-to-algorithm allocation problem , 2013, 2013 IEEE Congress on Evolutionary Computation.

[17]  Edmund K. Burke,et al.  Analyzing the landscape of a graph based hyper-heuristic for timetabling problems , 2009, GECCO.

[18]  Bruce A. Robinson,et al.  Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces , 2009, IEEE Transactions on Evolutionary Computation.

[19]  Andries Petrus Engelbrecht,et al.  Alternative hyper-heuristic strategies for multi-method global optimization , 2010, IEEE Congress on Evolutionary Computation.

[20]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[21]  Andries Petrus Engelbrecht,et al.  Metric-Based Heuristic Space Diversity Management in a Meta-Hyper-Heuristic Framework , 2014, 2015 IEEE Symposium Series on Computational Intelligence.

[22]  Giovanni Iacca,et al.  Parallel memetic structures , 2013, Inf. Sci..

[23]  Andries Petrus Engelbrecht,et al.  Investigating the use of local search for improving meta-hyper-heuristic performance , 2012, 2012 IEEE Congress on Evolutionary Computation.

[24]  Sanja Petrovic,et al.  Dispatching rules for production scheduling: A hyper-heuristic landscape analysis , 2009, 2009 IEEE Congress on Evolutionary Computation.

[25]  Xin Yao,et al.  Self-adaptive differential evolution with neighborhood search , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[26]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[27]  Andries P. Engelbrecht,et al.  Solution Space Diversity Management in a Meta-hyperheuristic Framework , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

[28]  Fei Peng,et al.  Population-Based Algorithm Portfolios for Numerical Optimization , 2010, IEEE Transactions on Evolutionary Computation.

[29]  Sanja Petrovic,et al.  The Cross-Domain Heuristic Search Challenge - An International Research Competition , 2011, LION.

[30]  Ender Özcan,et al.  Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space , 2013, 2013 IEEE Congress on Evolutionary Computation.

[31]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

[32]  Ville Tirronen,et al.  An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production , 2008, Evolutionary Computation.

[33]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[34]  Fabio Caraffini,et al.  An analysis on separability for Memetic Computing automatic design , 2014, Inf. Sci..

[35]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[36]  Ville Tirronen,et al.  Super-fit control adaptation in memetic differential evolution frameworks , 2009, Soft Comput..

[37]  Andries Petrus Engelbrecht.  Scalability of a heterogeneous particle swarm optimizer , 2011, 2011 IEEE Symposium on Swarm Intelligence.

[38]  Andries Petrus Engelbrecht,et al.  An analysis of heterogeneous cooperative algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[39]  Eduardo Segredo,et al.  Memetic algorithms and hyperheuristics applied to a multiobjectivised two-dimensional packing problem , 2014, J. Glob. Optim..

[40]  Michèle Sebag,et al.  Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed , 2010, GECCO '10.

[41]  Sanja Petrovic,et al.  A graph-based hyper-heuristic for educational timetabling problems , 2007, Eur. J. Oper. Res..

[42]  Graham Kendall,et al.  Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems , 2013, IEEE Transactions on Evolutionary Computation.

[43]  Andries Petrus Engelbrecht,et al.  Heuristic space diversity management in a meta-hyper-heuristic framework , 2015, 2014 IEEE Congress on Evolutionary Computation (CEC).